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Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community

Abstract

Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in—and even dominate—the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

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Fig. 1: The kefir community undergoes extensive compositional change during milk fermentation.
Fig. 2: Metabolite changes during kefir fermentation depict niche dynamics.
Fig. 3: The growth performances of individual kefir community members highlight inter-species dependencies for milk colonization.
Fig. 4: Interactions between kefir community members are extensive and qualitatively differ between solid and liquid phases.
Fig. 5: Unravelling selected metabolic interactions in kefir.
Fig. 6: The kefir community exhibits a basecamp lifestyle.

Data availability

All of the data generated or analysed during this study are included in this published article (and its Supplementary Information files). The genomes of isolated kefir species are available in the National Center for Biotechnology Information database under the accession no. PRJNA375758. The metatranscriptomic sequencing reads can be accessed from the European Nucleotide Archive under project ID PRJEB37001. The metabolomics data are available from the MetaboLights database (www.ebi.ac.uk/metabolights/) via accession nos. MTBLS1823, MTBLS1829 and MTBLS1830. Genome-scale metabolic models for kefir bacteria can be found at github.com/cdanielmachado/kefir_models. Source data are provided with this paper.

Code availability

The custom scripts, models and databases used for metatranscriptomics analysis and genome-scale metabolic modelling are available at github.com/cdanielmachado/kefir_paper/. All of the other computer code used in data analysis is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the EMBL GeneCore facility for help with the (meta-)genomic sequencing, O. Ponomarova for help with collecting kefir grains, advice on cultivation and feedback on the manuscript, and K. Zirngibl for feedback on the manuscript. This work was sponsored by the German Ministry of Education and Research (BMBF; no. 031A601B) as part of the ERASysAPP project SysMilk, and by the Innovation Fund Denmark through the project Food Transcriptomics (grant no. 6150-00033A).

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Authors

Contributions

S.B. and Y.K. conceived the project, designed and performed the experiments, analysed the data and wrote the paper. R.A.T.M. performed the untargeted metabolomics experiments and data analysis, as well as the quantitative analysis of aspartate and proline. E.K. and M.M. performed the GC–MS and ion chromatography analysis. V.B. contributed to the amplicon, metagenome and messenger RNA sequencing. J.N. and B.T. contributed to the experimental design. R.N. oversaw the targeted metabolomics analysis. D.M. contributed to the amino acid profile analysis and interpretation. A.M., D.M. and G.Z. analysed the RNA-seq data. U.S. oversaw the exo-metabolome analysis. K.R.P. conceived the project, designed the experimental approach, oversaw the project and wrote the paper.

Corresponding author

Correspondence to Kiran Raosaheb Patil.

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Extended data

Extended Data Fig. 1 Additional information related to Fig. 1d.

a, Temporal dynamics of bacterial composition during fermentation in the kefir fermented milk assessed by 16 S amplicon sequencing. The non-isolates represent the sum of all species, which are not in the kefir isolate collection. The fermentation is split into 6 different stages depending on the abundance changes of the major species. The shaded area marks the data range. N = 4, biologically independent samples. b, Fitting of DNA concentration dynamics to sigmoid curve. N = 4, biologically independent samples, error bars = mean values + /- SD. c, DNA extracted from fermentation samples. DNA concentration estimates, measured using Qubit (Supplementary Table 19), were used to determine absolute abundances shown in Fig. 1d. Raw gel images are depicted in Supplementary Fig. 3.

Extended Data Fig. 2 Amino acid dynamics in milk and kefir fermented milk.

a, Comparison of amino acid composition of milk total protein with that of free amino acids observed in kefir after 12 h, 40 h, and 90 h fermentation. Milk total protein amino acid composition used is average from two previous reports (Park, 2007; Schönfeldt et al., 2011)35,36. Lines depict the best linear fit and grey shading the 95% confidence interval of the linear fit. b, Comparison of expected accumulation (red dotted lines) and measured concentrations (blue lines) of amino acids in milk kefir. Green bars indicate the model-based estimation of uptake (negative values) and secretion (positive values) by kefir microbes.

Extended Data Fig. 3 Effect of EDTA and protein addition on grain wet-weight gain after 72 h fermentation.

Kefir grains grown in whey harvested after 36 h fermentation reveal decreased growth that is restored by casein supplementation. Addition of EDTA inhibits grain growth in both milk and casein-supplemented kefir whey. N = 4, error bars =SD.

Extended Data Fig. 4 Effect of acetate on growth of K. exigua, R. mucilaginosa, S. unisporus, and K. marxianus. S. unisporus and K. marxianus profit from low acetate concentrations.

K. exigua and R. mucilaginosa, are inhibited even by small acetate supplements. Changes in species growth are assessed relative to growth in non-supplemented milk whey. N = 4, biologically independent samples, error bars = mean values +/− SD.

Extended Data Fig. 5 Lactate concentration shapes consecutive time-windows of growth of Kazachstania exigua (yeast) and Acetobacter fabarum.

a, Evolution of lactate and acetate concentration during kefir fermentation. Different symbols mark data from replicates (N = 4). Colored block arrows indicate optimal lactate concentration ranges for the K. exigua and A. fabarum (Fig. 3d). Dotted lines connect the time-windows corresponding to these concentration ranges to the time-windows in panel B. b, Growth of kefir species over time with dotted lines marking the time-windows corresponding to the lactate concentration ranges from (a). c, Growth of K. exigua and A. fabarum in kefir spent whey harvested at different time points (N = 4 biologically independent samples; error bars = SD). Data are presented as mean values +/− SD.

Extended Data Fig. 6 Interaction network between kefir species based on milk acidification assay.

a, Schematic depiction of the method used to map metabolic interactions based on fermentation acidification kinetics. Species were grown in 96-well plates alone or in pairs; acidification of milk was assessed with a soluble pH-indicator. Positive interactions were identified as those that showed increased acidification in co-culture compared to mono-cultures, while negative interactions as those that showed decreased acidification in co-culture. b, Network of metabolic interactions between kefir species (Interaction calling based on N = 6; 3 biological and 2 technical replicates). Node sizes indicate number of interactions. Raw R-values extracted from scan images are provided in Supplementary Table 24.

Extended Data Fig. 7 Kefir grain growth profits from rare species and supplements.

Different kefir species were supplemented to the UHT-milk used for kefir propagation in this study (Methods). This suspension was then used as a medium to grow kefir grains in. The gain in wet-weight after 3 passages (circa 1 week) was then compared to kefir grains grown in milk and milk supplemented with proteinase K and yeast extract, respectively. Compared to the negative control, many rare species and few main kefir species supported the growth of the kefir grain. However, the effect of addition of proteinase K and yeast extract to the milk medium had the biggest effect on grain growth. Significance estimated by using two-sided t-test, p-values: * <0.05, ** <0.01, *** <0.001. N = 4 biologically independent samples, data are presented as mean values + /- SD. P-values: L. mesenteroides, 0,045; L. lactis (SB-150), 0,00034; S. haemolyticus, 0,0026; R. dentocariosa, 0,0012; Rhodotorula, 0,033; proteinase K, 0,00018; yeast extract, 8,83351E-07.

Extended Data Fig. 8 Prevalence of positive and negative interactions between kefir species in milk and on milk plates.

The outside layer shows distribution of interactions in milk (liquid), the inner layer for the milk plates (solid).

Extended Data Fig. 9 Integrated view of metabolite cross-feeding between, left: L. kefiranofaciens and L. mesenteroides, and right: L. lactis and A. fabarum, based on genome-scale metabolic modeling, gene expression data and metabolite measurements.

The colored metabolic maps connected to species mark reactions in the metabolic network that are assessed to be up- or down- regulated in co-cultures.

Extended Data Fig. 10 Kefir community shift when passaged using kefir fermented milk as an inoculum instead of the kefir grain.

Shown is the relative abundance of the bacterial members of the kefir community before and after five passages.

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Blasche, S., Kim, Y., Mars, R.A.T. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat Microbiol 6, 196–208 (2021). https://doi.org/10.1038/s41564-020-00816-5

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